STATA 15 - IC versions & prices


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STATA's expertise lies in the analysis of time based data. STATA provides not only the basic time series models like ARIMA but even the multivariate equivalents (VAR/VEC-Models) as well. Further you are able to model volatility using GARCH-models in STATA. Kaplan-Meier-curves are the way to analyse survival times, while mixed models help to analyse panel data. A mighty scripting language completes the package.

STATA produces all kinds of classical statistics. You can use it for descriptive statistics, hypothesis testing and visualization of data. Typically STATA is used in research and development. The large amount of different statistical methods helps scientists in all fields of applications (Social science, econometrics, epidimiology, medical research).

No matter if you are a student or a senior researcher, there is always the right version of STATA available: STATA IC, STATA SE and STATA MP

Arguments for Stata:

  • Used in research and development
  • Wide range of statistical and graphical methods
  • Comprehensive statistical software
  • Flexible and especially powerful for analysis of time series
  • Easy to learn but mighty scripting language

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Stata statistical software is a complete, integrated statistical software package that provides everything you need for data analysis, data management, and graphics. Stata is not sold in modules, which means you get everything you need in one package.

Easy to learn yet fully programmable for the most demanding data management and statistical requirements.

With Stata's menus and dialogs, you can easily point and click or drag and drop your way to all of Stata's statistical, graphical, and data management features. You can completely reshape your data, create group-level variables for panel or longitudinal data, graph a receiver operating characteristics (ROC) curve or impulse-response function (IRF), perform a case-control analysis, estimate a random-effects count-data model or a Cox proportional hazards model, or compute marginal effects from a nonlinear estimator. You can even access the dialog boxes for each command directly from the online help system. T his is a great way to explore all of the capabilities of Stata.

STATA Software is available in 3 different editions

Whether you're a first-year graduate student or a seasoned research professional, we have a package designed to suit your needs:

  • STATA/MP: The fastest version of Stata (for dual-core and multicore/multiprocessor computers)
  • STATA/SE: STATA for large datasets
  • Stata/IC: The standard version of STATA

Feature Comparison

Edition Max. Variables Max. right-hand Variables Max. Observations 64-Bit Version available? Parallel Processing
Stata/MP 32767 10998 unlimited* Yes Yes
Stata/SE 32767 10998 unlimited* Yes No
Stata/IC 2047 798 unlimited* Yes No

* The maximum number of observations is limited only by the amount of available RAM on your system.

STATA scripting language

STATA's scripting language is easy to learn and helps you to get the most out of your data. It allows not only to use and modify the existing routines to generate standard reports, but can easily be extended with newly created statistical functions.

Efficient Datamanagent with STATA

Datamanagement with STATA is easy and efficient. Joining datasets, creating new variables or producing summary tables is done in no time.

Professional Graphics with STATA

STATA provides professional graphics that can directly be used for documents and publications. This includes not only pre-defined standard graphs but although highly customizeable graphics.

Further Information:

Trialversion of STATA

The producer provides a free 30-day trialversion on their website. The trialversion contains all the features of STATA. You can register for this license simply by visiting the following link:

  Windows Mac Linux
Further Requirements     Stata for Unix requires a video card that can display thousands of colors or more (16-bit or 24-bit color)
Operating System Windows XP, Vista, 7, 8, 10, Windows Server 2003, 2008, 2012 (32-/64-Bit) Mac OS X 10.7 or higher (64-Bit) Any 64-bit (x86-64 or compatible) or 32-bit (x86 or compatible) running Linux
Min. CPU  
Min. RAM 512 MB
Disk Space 900 MB

New Features in STATA 15

Technical Innovations:

  • Unicode-Support (e.g. variable names, data, value label, variable label)
  • Hypothetical limit on number of observations now stands at 281 trillion observations (exact number depends on RAM size and number of variables)
  • Support for ICD-10 codes©

New Statistics:

  • Bayesian Analysis:
  • 12 built-in likelihood models for continuous, binary, ordinal and count data Outcomes
  • 22 built-in a priori distributions, which can be continuously, univariate, multivariate or discreet
  • Metropolis-Hastings-, Gibbs -Algorithm
  • Item Response Theory:
  • for binary, categorical, ordinal items, and combinations thereof
  • Postestimation Graphics (ICC, CCC, IIF, TCC, TIF)

Innovations and enhancements to the following statistical methods

  • Treatment Effects:
  • Survival treatment effects
  • Endogenous treatment effects
  • Check for balance between treatment and control groups
  • Probability weights for many commands
  • Multilevel Mixed Effects Model:
  • New multilevel Estimator: Parametric Survival Models
  • Linear Mixed-Effects-Models with Small-Sample Denominator Degrees of Freedom
  • Panel data:
  • New random effects Survival Estimator
  • Options VCE (robust) and VCE (cluster clustvar) now available for new and existing Estimator
  • Structural Equation Modeling:
  • Multivariate survival models
  • Survival models with unobserved components
  • Survival models combined with other types of outcomes
  • Fraction response models with unobserved components
  • Fraction response models combined with other types of outcomes
  • Time series:
  • Markov-switching models
  • Tests for structural Breaks
  • Power analysis:
  • new power commands for contingency

and much more!

What's New in STATA 13?

Treatment effects

  • Inverse probability weights (IPW)
  • Regression adjustment
  • Propensity-score matching
  • Covariate matching
  • Doubly robust methods
  • Continuous, binary, and count outcomes

Multilevel/mixed models

  • Negative binomial
  • Ordered logistic
  • Ordered probit
  • Multinomial logistic
  • GLM

Long strings

  • 2 billion characters
  • Text strings
  • Binary large objects (BLOBs)
  • Import/Export/ODBC/SQL
  • Work just like Stata strings

Generalized SEM

  • Generalized linear responses: binary, count, ordered outcomes
  • Multilevel/hierarchical models: nested and crossed models
  • Random slopes and intercepts
  • Fast

Power and sample size

  • Means, proportions, variances, correlations/li>
  • Case–control and cohort studies
  • Interactive control panel
  • Tabular results
  • Automatic graphs


  • Time series and panels
  • One to thousands of equations
  • Identities
  • Add factors
  • Dynamic and static
  • CIs via stochastic simulation
  • Compare scenarios

Effect sizes

  • Means
  • Linear regression
  • Confidence intervals
  • Cohen's d, Hedges's g, Glass's ?, η², ω²

Panel data

  • Ordered outcomes
  • RE ordered probit
  • RE ordered logistic
  • Cluster–robust SEs to relax distributional assumptions and allow for correlated data

Project Manager

  • Organize files (1-10,000)
  • Multiple projects
  • Filter on filenames
  • Click to open
  • Click to run

More statistics

  • Ordered probit with selection
  • Poisson with endogenous covariates
  • Robust SEs for quantile regression
  • ML estimation without programming
  • Fractional-polynomial prefix

More documentation

  • Treatment-effects manual
  • Multilevel mixed-effects manual
  • Power and sample-size manual
  • 2,000 more pages
  • 11,000+ total pages

And more

  • Factor variables show labels
  • Import delimited with preview
  • Import from Haver Analytics
  • Business calendars from data
  • Java plugin API
  • FTP and secure HTTP

Stata Features

Data management

data transformations, match-merge, ODBC, XML, by-group processing, append files, sort, row–column transposition, labeling, saving results

Basic statistics

summaries, cross-tabulations, correlations, t tests, equality-of-variance tests, tests of proportions, confidence intervals, factor variables

Linear models

regression; bootstrap, jackknife, and robust Huber/White/sandwich variance estimates; instrumental variables; three-stage least squares; constraints; quantile regression; GLS

Multilevel mixed-effects models

generalized linear models;continuous, binary, and count outcomes; two-, three-, and higher-level models; random-intercepts; random-slopes; crossed random effects; BLUPs of effects and fitted values; hierarchical models; residual error structures; support for survey data in linear models

Binary, count, and discrete outcomes

logistic, probit, tobit; Poisson and negative binomial; conditional, multinomial, nested, ordered, rank-ordered, and stereotype logistic; multinomial probit; zero-inflated and left-truncated count models; selection models; marginal effects

Longitudinal data/panel data

random and fixed effects with robust standard errors; linear mixed models, random-effects probit, GEE, random- and fixed-effects Poisson, dynamic panel-data models, and instrumental-variables regression; panel unit-root tests; AR(1) disturbances

Generalized linear models (GLMs)

ten link functions, user-defined links, seven distributions, ML and IRLS estimation, nine variance estimators, seven residuals

Nonparametric methods

Wilcoxon-Mann-Whitney, Wilcoxon signed ranks and Kruskal-Wallis tests; Spearman and Kendall correlations; Kolmogorov-Smirnov tests; exact binomial CIs; survival data; ROC analysis; smoothing; bootstrapping

Exact statistics

exact logistic and Poisson regression, exact case-control statistics, binomial tests, Fisher's exact test for r × c tables


balanced and unbalanced designs; factorial, nested, and mixed designs; repeated measures; marginal means; contrasts

Multivariate methods

factor analysis, principal components, discriminant analysis, rotation, multidimensional scaling, Procrustean analysis, correspondence analysis, biplots, dendrograms, user-extensible analyses

Cluster analysis

hierarchical clustering; kmeans and kmedian nonhierarchical clustering; dendrograms; stopping rules; user-extensible analyses

Resampling and simulation methods

bootstrapping, jackknife and Monte Carlo simulation; permutation tests

Tests, predictions, and effects

Wald tests; LR tests; linear and nonlinear combinations, predictions and generalized predictions, marginal means, least-squares means, adjusted means; marginal and partial effects; forecast models; Hausman tests


line charts, scatterplots, bar charts, pie charts, hi-lo charts, regression diagnostic graphs, survival plots, nonparametric smoothers, distribution Q-Q plots

Survey methods

multistage designs; bootstrap, BRR, jackknife, linearized, and SDR variance estimation; poststratification; DEFF; predictive margins; means, proportions, ratios, totals; summary tables; regression, instrumental variables, probit, Cox regression

Survival analysis

Kaplan-Meier and Nelson-Aalen estimators,; Cox regression (frailty); parametric models (frailty); competing risks; hazards; time-varying covariates; left- and right-censoring, Weibull, exponential, and Gompertz analysis


standardization of rates, case–control, cohort, matched case-control, Mantel-Haenszel, pharmacokinetics, ROC analysis, ICD-9-CM

Time series

ARIMA; ARFIMA; ARCH/GARCH; VAR; VECM; multivariate GARCH; unobserved components model; dynamic factors; state-space models; business calendars; correlograms; periodograms; forecasts; impulse-response functions; unit-root tests; filters and smoothers; rolling and recursive estimation

Multiple imputation

nine univariate imputation methods; multivariate normal imputation; chained equations; explore pattern of missingness; manage imputed datasets; fit model and pool results; transform parameters; joint tests of parameter estimates; predictions

Simple maximum likelihood

specify likelihood using simple expressions; no programming required; survey data; standard, robust, bootstrap, and jackknife SEs; matrix estimators

Programmable maximum likelihood

user-specified functions; NR, DFP, BFGS, BHHH; OIM, OPG, robust, bootstrap, and jackknife SEs; Wald tests; survey data; numeric or analytic derivatives

Other statistical methods

kappa measure of interrater agreement; Cronbach's alpha; stepwise regression; tests of normality

Programming features

adding new commands; command scripting; object-oriented programming; menu and dialog-box programming; Project Manager; plugins

Matrix programming-Mata

interactive sessions, large-scale development projects, optimization, matrix inversions, decompositions, eigenvalues and eigenvectors, LAPACK engine, real and complex numbers, string matrices, interface to Stata datasets and matrices, numerical derivatives, object-oriented programming

Internet capabilities

ability to install new commands, web updating, web file sharing, latest Stata news


Section 508 compliance, accessibility for persons with disabilities

Sample session

A sample session of Stata for Mac, Unix, or Windows.

User-written commands

User-written commands for meta-analysis, data management, survival, econometrics

Graphical user interface

menus and dialogs for all features; Data Editor; Variables Manager; Graph Editor; Project Manager; Do-file Editor; Clipboard Preview Tool; multiple preference sets


line charts; scatterplots; bar charts; pie charts; hi-lo charts; contour plots; GUI Editor; regression diagnostic graphs; survival plots; nonparametric smoothers; distribution Q-Q plots


20 manuals20 manuals; 11,000+ pages; seamless navigation; thousands of worked examples; methods and formulas; references; 11,000+ pages; seamless navigation; thousands of worked examples; methods and formulas; references

Power and sample size

power; sample size; effect size; minimum detectable effect; means; proportions; variances; correlations; case-control studies; cohort studies; survival analysis; balanced or unbalanced designs; results in tables or graphs

Treatment effects

inverse probability weight (IPW); doubly robust methods; propensity score matching; regression adjustment; covariate matching; multilevel treatments; average treatment effects (ATEs); average treatment effects on the treated (ATETs); potential-outcome means (POMs)

SEM (Structural equation modeling)

graphical path diagram builder; standardized and unstandardized estimates; modification indices; direct and indirect effects; continuous, binary, count, and ordinal outcomes (GLM); multilevel models; random slopes and intercepts; factors scores, empirical Bayes, and other predictions; groups and tests of invariance; goodness of fit; handles MAR data by FIML; correlated data


statistical; random-number; mathematical; string; date and time

Embedded statistical computations

Numerics by Stata

Contrasts, pairwise comparisons, and margins

compare means, intercepts, or slopes; compare to reference category, adjacent category, grand mean, etc.; orthogonal polynomials; multiple comparison adjustments; graph estimated means and contrasts; interaction plots

GMM an nonlinear regression

generalized method of moments (GMM); nonlinear regression